Convolutional Neural Networks (CNNs) have become a cornerstone of computer vision, yet deploying them on embedded devices (robots, IoT systems, mobile hardware) remains challenging due to their large size and energy requirements. Model compression is a key solution to make these networks more efficient without severely impacting accuracy. Existing methods (such as weight quantization, low-rank factorization, and sparsity) show promising results but quickly reach their limits when used independently. This PhD will focus on designing a unified optimization framework that combines these techniques in a synergistic way. The work will involve both theoretical aspects (optimization methods, adaptive rank selection) and experimental validation (on benchmark CNNs like ResNet or MobileNet, and on embedded platforms such as Jetson, Raspberry Pi, and FPGA). An optional extension to transformer architectures will also be considered. The project benefits from complementary supervision: academic expertise in tensor decompositions and an industrial-oriented partner specialized in hardware-aware compression.